Retrieval of snow cover area by optical sensors Rune Solberg, NR Contributions from: Petra Malcher a

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Retrieval of snow cover area by optical sensors
Rune Solberg, NRContributions from Petra
Malcher and Helmuth Rott, IMGISari Mätsimäki,
SYKE Miia Eskelinen and Martti Hallikainen, HUT
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Background

• Parameter Snow Covered Area (SCA) for
  mountainous regions and the boreal forest zone
• Definition Snow/no snow or areal fraction of
  ground covered by snow
• State of the art Daily information can be
  retrieved from optical sensors giving data of 250
  m 1 km spatial resolution. Visible,
  near-infrared and thermal infrared part of the
  electromagnetic spectrum
• Motivation
• Snow cover is included in most hydrological
  models
• Important for water management and hydropower
• Important for flood prediction
• Important for energy balance calculations
• Problems
• Clouds
• Dynamic reflectance properties of snow
• Dense forest
• Steep terrain

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EnviSnow development The mountains

• Developed and validated a binary (snow/no snow)
  SCA algorithms for MODIS and MERIS in the Alps
• Developed and validated a new fractional SCA
  algorithm for MODIS in the Norwegian mountains

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MODIS Snow Classification

• automated binary classification
• constrained to quality controlled data
• confined to cloud free pixels
• lake map (250 m) prevents water bodies to be
  masked as snow

• Cloud
• CO2 threshold test
• BT35(13.9 mm) lt 236 K
• BT31-BT22 test
• BT31(11 mm ) - BT22(3.9 mm) lt -12 K
• Seasonal BT31 threshold test
• BT31(11 mm) lt 240 K 01, 240 K 02 ...
• Band 1 6 reflectance tests
• Bd6(1.64 mm)/Bd1(0.65 mm) gt 0.6
• Bd1(0.65 mm) gt 20

• Snow
• NDSI of bands 3 (0.469 mm) and 6 (1.640 mm)
• NDSI gt 0.4
• BT31 thermal mask
• BT31(11 mm ) lt 282 K
• Bd3 threshold
• Bd3 gt 6

22 April 2005, MODIS 250 m
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ASTER/MODIS Snow Cover Difference

• Difference map for 20 June 2003, wider Ötztal
  area
• Overestimates broken snow cover
• Underestimates isolated snow patches mainly along
  the snow rim
• Experience with forested areas
• Overall better performance for non-forested areas
• Percentage of SCA mapped by both sensors
  decreases with advancing snowmelt
• Progressive fragmentation of snow cover is the
  prime factor for the increase of snow fraction
  mapped by ASTER only

only MODIS SCA
only ASTER SCA
20 June 2003, Ötztal
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MERIS Snow Classification

• semi-automated binary algorithm
• constrained to quality controlled data
• clouds are manually selected
• confined to cloud free pixels
• main snow classifier is the multi- temporal
  ratio of band 3 (0.413 mm)
• lake map (250 m) prevents water bodies to be
  masked as snow

snow (others)
snow (forest)
clouds
snow free
water
bad input
27 April 2004
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Fractional SCA

• Typical problems with current operational
  algorithms
• Terrain effects
• Snow metamorphosis
• Anisotropic reflectance
• Snow impurities
• New algorithm
• Combines empirical and physical models for
  compensation of the effects

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Fractional SCA algorithm

• Prior SCA estimation
• Prediction of
• Metamorphosis
• Impurities
• Linear spectral unmixing
• Iterative

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Experimental results

• Landsat TM and Terra MODIS images acquired 30 May
  2004
• Late snowmelt season situation with very patchy
  snow
• Errors typically reduced from 40 in steep
  slopes to lt5
• Errors for snow with high content of impurities
  seen to be reduced from 15-20 (underestimation)
  to lt5

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EnviSnow development Boreal forests

• SCA algorithms for boreal forests expanded from
  AVHRR application to Terra MODIS and Envisat
  MERIS
• Operational dual-sensor SCA mapping using both
  AVHRR and MODIS best image is chosen (based on
  imaging geometry and cloud cover)
• Airborne spectrometer measurements conducted for
  determining snow reflectance properties in the
  melting season

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Serious floods in Lapland May23 May30
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Airborne spectrometer campaigns

• Airborne measurements conducted for studying snow
  reflectance properties in the melting season
• Tuusula Test Site in latitude 60 N
• Sodankyla-Lokka Test Site in latitude 67 N
• Objective to assist satellite based snow cover
  mapping algorithm development
• Statistical variability for various land surface
  reflectances in the boreal forest belt
• Study the varying viewing angle effect on the
  remote sensing reflectances with airborne
  spectrometer

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Results
(1)
(3)
(1)Thick forest (2)Sparse forest (3) Melting snow
(2)
(4) Snow

• The AISA airborne spectrometer data used for
  calculating statistics for snow and forest
  reflectances has a pixel size of 1 m x 1 m

• Here is a visualised example of the averaged AISA
  sample spectra collection for thick forest (1),
  sparse forest (2) and melting snow (3)

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(a)
(b)
(c)
(d)

• Spectra from the AISA dataset acquired on 27
  March 2002 in Tuusula. Averaged sample spectra
  for (a) tree crowns in dense forest, (b) shadow
  in dense forest, (c) dense forest and (d) sparse
  forest

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Spectra from the AISA dataset acquired on 4 May
2003 in Sodankyla-Lokka test area

• The effect of variable observation angle to the
  airborne spectrometer-derived radiance is
  demonstrated for (a) principal solar plane and
  (b) perpendicular plane
• The land cover types are () snow-covered dense
  forest, (o) snow-covered open area, (?)
  snow-covered bog and (x) snow free ground in
  wavelengths 551-555 nm

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Conclusions

• SCA algorithms for the mountains
• Binary SCA algorithms developed and validated for
  MODIS and MERIS in the Alps (IMGI)
• Fractional (at sub-pixel level) SCA algorithm
  developed and tested for MODIS in the Norwegian
  mountains tests indicate that it is
  significantly more accurate than current
  operationally used algorithm (NR)
• SCA algorithms for boreal forest
• Fractional (at basin level) SCA algorithm
  developed for boreal forest and validated for
  AVHRR, MODIS and MERIS in Finland (SYKE)
• Dual-sensor algorithm (AVHRR MODIS) in
  operational use (SYKE)
• Reflectance of snow and forest at varying density
  measured in Finland in the melting season to
  assist algorithm development (HUT)